Accenture
GCP Platform Architect
Accenture, Walnut Creek, California, United States, 94598
Are you ready to step up to the New and take your technology expertise to the next level?
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As part of our Google Cloud Platform practice, you will lead technology innovation for our clients through robust delivery of world-class solutions. There will never be a typical day and that’s why people love it here. The opportunities to make a difference within exciting client initiatives are unlimited in the ever-changing technology landscape. You will be part of a growing network of technology experts who are highly collaborative taking on today’s biggest, most complex business challenges. We will nurture your talent in an inclusive culture that values diversity. Come grow your career in technology at Accenture!
The Senior Google Cloud Platform (GCP) Platform Architect is responsible for leading the design, implementation and management of GCP infrastructures and solutions. In this role, you will play a critical part in shaping, implementation and support of client’s cloud platforms, ensuring scalability, security and optimal performance.
Key responsibilities may include:
Cloud Architecture Design and Implementation:
Define and implement cloud architecture strategies and roadmaps aligned with business goals and technical requirements. Design scalable, highly available, and fault-tolerant GCP architectures using best practices and patterns. Evaluate and select appropriate GCP services and technologies to meet specific project needs. Create and maintain architectural diagrams, documentation, and blueprints.
Cloud Migration and Modernization:
Develop and execute comprehensive cloud migration plans, including assessment, planning, migration, and optimization phases. Refactor and modernize existing applications and infrastructure for optimal performance on GCP. Identify and mitigate risks associated with cloud migration.
Infrastructure Management:
Provision, configure, and manage GCP resources using Infrastructure as Code (IaC) tools and automation frameworks. Monitor and maintain the health and performance of GCP infrastructure, proactively identifying and resolving issues. Implement cost optimization strategies to ensure efficient utilization of GCP resources. Implement monitoring, logging, and alerting solutions for effective infrastructure management.
Automation and DevOps:
Design and implement CI/CD pipelines for automated deployment and management of GCP resources. Foster a culture that promotes collaboration between development, operations, and security teams.
Performance Optimization:
Conduct performance testing and analysis to identify and address bottlenecks in GCP infrastructure and applications. Fine-tune GCP configurations and settings to optimize performance and efficiency. Understand network infrastructure, load balancing, and auto-scaling strategies to handle varying workloads.
Security and Compliance:
Design and implement security controls and measures to protect GCP infrastructure and data from unauthorized access, threats, and vulnerabilities. Ensure compliance with industry regulations and data privacy standards. Conduct regular security audits and assessments to identify and remediate risks.
Collaboration and Mentorship:
Work closely with development, operations, and security teams to ensure seamless integration and collaboration on GCP projects. Provide technical guidance and mentorship to other engineers and architects on GCP best practices and technologies. Stay up-to-date with the latest GCP developments and industry trends, sharing knowledge with the team.
Travel may be required for this role. The amount of travel will vary from 25% to 100% depending on business need and client requirements. Basic Qualifications: Minimum of 8 years (or equivalent experience) in architecture design and technical leadership in a cloud environment. Minimum of 3 years of experience in Google Cloud Platform (GCP). Proven experience in leading teams (minimum of 5 people) to design and implement cloud foundations. In-depth understanding of Google Cloud services such as Compute Engine, Kubernetes Engine, Cloud Storage, BigQuery, and more. Strong knowledge of cloud security best practices, networking, and infrastructure as code (e.g., Terraform). Bachelor's degree or equivalent (minimum 10 years work experience). (If Associate’s Degree, must have a minimum 6 years work experience). Excellent problem-solving skills and the ability to work effectively in a fast-paced environment. Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders. Preferred: Google Certifications (Google Certified Professional Cloud Architect, Google Certified Data Architect, or Google Certified Associate Cloud Engineer) – There is an expectation that a certification in Google Cloud will be necessary after hire. GCP Expertise: Extensive experience with GCP data services, including BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and related technologies. Data Warehousing: Strong proficiency in data warehousing concepts, data modeling, and ETL/ELT processes. Big Data: Experience with big data technologies such as Hadoop, Spark, and NoSQL databases. Data Security: Deep understanding of data security principles and best practices on GCP. Vertex AI: Proficiency in using Google Cloud's Vertex AI platform for building, deploying, and managing machine learning models, including GenAI models. Generative AI Studio: Experience with Generative AI Studio for prototyping and experimenting with generative AI models. Model Garden: Familiarity with Google's Model Garden and its offerings for accessing and deploying pre-trained GenAI models. MLOps for GenAI: Experience in implementing MLOps practices for the development, deployment, and monitoring of GenAI models. Cloud Architecture: Proven track record in designing and implementing cloud-based data architectures.
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Define and implement cloud architecture strategies and roadmaps aligned with business goals and technical requirements. Design scalable, highly available, and fault-tolerant GCP architectures using best practices and patterns. Evaluate and select appropriate GCP services and technologies to meet specific project needs. Create and maintain architectural diagrams, documentation, and blueprints.
Cloud Migration and Modernization:
Develop and execute comprehensive cloud migration plans, including assessment, planning, migration, and optimization phases. Refactor and modernize existing applications and infrastructure for optimal performance on GCP. Identify and mitigate risks associated with cloud migration.
Infrastructure Management:
Provision, configure, and manage GCP resources using Infrastructure as Code (IaC) tools and automation frameworks. Monitor and maintain the health and performance of GCP infrastructure, proactively identifying and resolving issues. Implement cost optimization strategies to ensure efficient utilization of GCP resources. Implement monitoring, logging, and alerting solutions for effective infrastructure management.
Automation and DevOps:
Design and implement CI/CD pipelines for automated deployment and management of GCP resources. Foster a culture that promotes collaboration between development, operations, and security teams.
Performance Optimization:
Conduct performance testing and analysis to identify and address bottlenecks in GCP infrastructure and applications. Fine-tune GCP configurations and settings to optimize performance and efficiency. Understand network infrastructure, load balancing, and auto-scaling strategies to handle varying workloads.
Security and Compliance:
Design and implement security controls and measures to protect GCP infrastructure and data from unauthorized access, threats, and vulnerabilities. Ensure compliance with industry regulations and data privacy standards. Conduct regular security audits and assessments to identify and remediate risks.
Collaboration and Mentorship:
Work closely with development, operations, and security teams to ensure seamless integration and collaboration on GCP projects. Provide technical guidance and mentorship to other engineers and architects on GCP best practices and technologies. Stay up-to-date with the latest GCP developments and industry trends, sharing knowledge with the team.
Travel may be required for this role. The amount of travel will vary from 25% to 100% depending on business need and client requirements. Basic Qualifications: Minimum of 8 years (or equivalent experience) in architecture design and technical leadership in a cloud environment. Minimum of 3 years of experience in Google Cloud Platform (GCP). Proven experience in leading teams (minimum of 5 people) to design and implement cloud foundations. In-depth understanding of Google Cloud services such as Compute Engine, Kubernetes Engine, Cloud Storage, BigQuery, and more. Strong knowledge of cloud security best practices, networking, and infrastructure as code (e.g., Terraform). Bachelor's degree or equivalent (minimum 10 years work experience). (If Associate’s Degree, must have a minimum 6 years work experience). Excellent problem-solving skills and the ability to work effectively in a fast-paced environment. Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders. Preferred: Google Certifications (Google Certified Professional Cloud Architect, Google Certified Data Architect, or Google Certified Associate Cloud Engineer) – There is an expectation that a certification in Google Cloud will be necessary after hire. GCP Expertise: Extensive experience with GCP data services, including BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and related technologies. Data Warehousing: Strong proficiency in data warehousing concepts, data modeling, and ETL/ELT processes. Big Data: Experience with big data technologies such as Hadoop, Spark, and NoSQL databases. Data Security: Deep understanding of data security principles and best practices on GCP. Vertex AI: Proficiency in using Google Cloud's Vertex AI platform for building, deploying, and managing machine learning models, including GenAI models. Generative AI Studio: Experience with Generative AI Studio for prototyping and experimenting with generative AI models. Model Garden: Familiarity with Google's Model Garden and its offerings for accessing and deploying pre-trained GenAI models. MLOps for GenAI: Experience in implementing MLOps practices for the development, deployment, and monitoring of GenAI models. Cloud Architecture: Proven track record in designing and implementing cloud-based data architectures.
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